【作者】 易建波；

【导师】 黄琦；

【作者基本信息】 电子科技大学 ， 检测技术与自动化装置， 2013， 博士

【摘要】 随着国民经济的持续发展,我国电能消耗量持续增长,电网规模不断扩大,电力系统稳定性问题更加复杂,分析和控制难度不断增加。其中,低频振荡问题在互联电力系统中屡有发生,对电力系统运行稳定性造成严重影响,已成为目前威胁互联电网安全和制约电网传输能力的关键问题。传统的低频振荡分析方法,基于电力系统结构模型,以小干扰稳定性理论分析振荡模式,或者以计及非线性高阶项交互影响的正规型方法和模态级数法分析振荡模式。这些方法的分析精度受制于模型阶数、参数准确性、未建模动态以及模式交互因素的影响,已无法满足现代大规模互联电力系统稳定性分析的要求。目前,随着WAMS (Wide-Area Measurement System, WAMS)系统在电网中的应用,基于测量信号的低频振荡分析方法成为研究重点。针对实测低频振荡信号,受扰轨迹真实反映了系统的动态过程,并且完全包含激振模式的特征信息。但是低频振荡信号是一种典型的非平稳信号,存在振幅随时间变化以及激振模式出现随机性等非平稳信号特点,传统低频振荡信号分析方法基于平稳信号假设模型,对低频振荡模式的非平稳特性参数辨识存在一定的局限性。基于此,本文研究HHT (Hilbert Huang Transform, HHT)方法分析低频振荡问题,围绕该方法应用中的主要理论和关键技术问题开展以下几方面的研究：1.研究基于测量信号的低频振荡分析方法。针对低频振荡实测信号的特点,对比研究改进Prony方法和HHT方法分析低频振荡信号的理论模型和算法过程,测试分析了Prony方法存在模型定阶、拟合精度以及非平稳参数辨识局限性问题。着重研究了HHT方法分析振荡信号及辨识振荡模式特征参数的方法,讨论振荡模式辨识的物理意义,测试验证了该方法的模式辨识结果具有较高的有效性和精度。2.讨论影响HHT方法分析低频振荡信号有效性和准确性的主要因素,研究提高HHT方法性能的关键技术和改进方法。针对现有HHT方法存在的端点效应、拟合误差、模式混叠和阻尼损失等问题,通过低通数字滤波技术、基于AR模型的端点延拓技术、B样条插值技术、筛分控制因子和复小波分析等改进技术,实现了一种改进的低频振荡信号分析算法,极大地提高了振荡模式特征参数辨识的精度。3.针对WAMS提供的实测信号,研究基于HHT方法的在线低频振荡模式分析技术。特别针对EMD (Empirical Mode Decomposition, EMD)过程的模式混叠和低频振荡信号的非平稳参数辨识问题,提出改进频率外差方法和自适应滑动窗口技术,解决了EMD分解中的间歇性和倍频模式混叠问题,同时确定出不同振荡模式的存在时间,并且实现了1s时间进制的近实时低频振荡信号在线分析算法。该算法能够在线自适应分析出系统低频振荡情况,辨识低频振荡模式参数准确度高、实时性强。4.讨论了基于模型的小干扰稳定性理论在判定振荡模式相关性方面的局限性。研究基于实测信号分析振荡模式相关性,从能量转换的角度建立一种基于HHT方法的贡献因子,量化定义了扰动与低频振荡模式和发电机状态变量之间的相关性关系。设计了基于HHT方法的贡献因子在线算法,通过实例验证了该方法分析与弱阻尼振荡模式和功率迫振模式相关的振荡源定位有效性,进一步完善了HHT方法分析低频振荡问题的理论体系,拓展了该方法的应用范围。更多还原

【Abstract】 With the development of our national economy, energy consumption and grid scale continue to grow. This makes the stability problems in power system become more complex and its analysis and control are getting harder. In fact, the low frequency oscillation in power system which seriously affects the stable operation of the power system has repeatedly occurred and become one of the key issues which threaten the safety and stability of interconnected power grid and restrict the power transmission capacity.Traditional methods are based on the power system structure model, such as applying the small signal stability theory to analyze low frequency oscillation modes or Normal Form and modal series method with the non-linear higher order terms considered to analyze low frequency oscillation modes. The analysis accuracy of these methods is subject to the order of the model, the parameters accuracy, un-modeled dynamic and interactive modal factors, so that traditional low frequency oscillation analysis methods have been unable to meet the requirements of stability analysis of modern power system. Nowadays, with the extensive application of WAMS (Wide-Area Measurement System) in the wide area power grid, the analysis of low frequency oscillation of power system based on the measurement signals becomes a hot research topic.With measured low frequency oscillation signals, the disturbed trajectories reflect the true dynamic process of the system and contain the complete characteristic information of the excitation modes. However, the low frequency oscillation signal is a typical non-stationary signal, which demonstrates non-stationary signal characteristics such as the amplitude changing over time and the random emergence of excitation modes. Traditional measurement signal based analysis methods assume the signal is stable, which limits its use to analyze non-stationary characteristics of the low frequency oscillation modes. Therefore, the data-driven HHT (Hilbert Huang Transform) method is proposed to analyze the low frequency oscillation. In this paper, the main theories and key technical issues of HHT method are studied as following: 1. Low frequency oscillation analysis based on the measured signals. Comparative study is performed on theoretical models and algorithms process of the improved Prony method and the HHT method analysis of low frequency oscillation signal. The limitation of Prony analysis to analyze low frequency oscillation signal is tested and discussed, such as model order, fitting precision and non-stationary parameters identification. Using the HHT method to deal with non-stationary oscillation signal and identify oscillation mode characteristic parameters is studied in detail, including discussing the physical meaning of the method to identify the low frequency oscillation mode and analyzing the results validity and accuracy of low-frequency oscillation mode parameters identification.2. Analyze the main factors affecting the validity and accuracy of the low frequency oscillation signal based on HHT method, then some key technologies and improved methods to improve the performance of HHT method are studied in detail. For the problem of end effect, fitting error, mode mixing and damping loss existed in HHT method, the low-pass digital filtering technology, the endpoint extension technology based on AR model, the B-spline interpolation technologies, the sifting control factors and complex wavelet analysis method are proposed to achieve an improved EMD (Empirical Mode Decomposition) process, which greatly improves the accuracy of low frequency oscillation mode characteristic parameters identification.3. Especially in terms of real-time measurement oscillation signals provided by WAMS, the online low frequency oscillation analysis techniques based on HHT is studied in detail. As the EMD process has mode mixing and non-stationary parameter identification problems in low frequency oscillation signal, improved frequency heterodyne method and adaptive sliding window technologies are proposed to deal with the intermittent and octave mode mixing problem within EMD decomposition process. The proposed method is able to determine the presence time of different low frequency oscillation modes and implement the Is hexadecimal near-real-time analysis and identification algorithm of low frequency oscillation signal. The algorithm can adaptively implement the online analysis of measured signal of low frequency oscillation, which has high accuracy and instantaneity to identify the low frequency oscillation mode parameters.4. The limitations of small signal stability analysis theory to analyze the low frequency oscillation modes correlation with the structure-based model is discussed in detail. The contribution factors, considered from the perspective of energy conversion are established based on the HHT method and measured oscillation signals, which can define the correlation between the disturbance and the low frequency oscillation mode quantitatively. Furthermore, an online algorithm to compute contribution factors based on the HHT method is proposed to solve the correlation problem between the source generators and the oscillation mode when weakly damped oscillation mode and forced oscillation mode appear. The proposed method can further consolidate the theoretical base of low frequency oscillation analysis based on the HHT method and expand its applications.更多还原